Retail Chain Transforms Customer Experiences with BI Acceleration
Company Size
1,000+
Region
- America
Country
- United States
Product
- Kyvos BI acceleration platform
- Tableau
- MicroStrategy
Tech Stack
- Azure Databricks
- Azure Data Lake Storage (ADLS)
- Impala
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Brand Awareness
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Big Data Analytics
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Retail
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Demand Planning & Forecasting
- Inventory Management
- Supply Chain Visibility
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
The customer is a leading US-based grocery chain that has carved a niche by building a loyal customer base. They have hundreds of stores and thousands of products, with daily visitors resulting in almost 140,000 transactions per day. The company is known for its unique shopping experience and customer loyalty. They wanted to use their data to understand their customers better and maintain their market-leader position. To offer a unique shopping experience to each customer, they decided to align their merchandise mix and store inventory to match each customer’s specific needs.
The Challenge
The grocery chain wanted to use their data to understand their customers better and maintain their market-leader position. They decided to align their merchandise mix and store inventory to match each customer’s specific needs. With hundreds of stores, thousands of products, and daily visitors, the store had details on almost 140,000 transactions per day and wanted to use this data to support their business decisions. However, their current environment could not handle the data scale and complexity, making it impossible to conduct Year-over-Year, much less Month-over-Month analysis. They resorted to writing complex queries using Impala to fetch data from their Cloudera platform. But when they tried to join tables with more than one billion cardinalities, Impala usually timed out.
The Solution
The company evaluated several options for almost three years, ultimately choosing Kyvos as their BI acceleration platform. Kyvos created a universal semantic layer on Azure to solve complex use cases and get quick answers to business questions. Smart OLAP™ technology helped them pre-aggregate two years of historical data, leveraging Azure Databricks service and ADLS for storage. Analysts and business users could plug their Tableau and MicroStrategy dashboards into Kyvos and conduct self-service, interactive analysis on enterprise data with 100x faster performance than Impala.
Operational Impact
Quantitative Benefit
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